An Excel spreadsheet listing the information recorded on each of 18,686 costume designs can be viewed, downloaded, and explored. All the usual Excel sorting possibilities are available, and in addition a useful filter has been installed. For example, to find the number of designs that are Frieze Type #1, go to the top of the frieze type 2 column (column AS), click on the drop-down arrow and unselect every option box except True (i.e. True should be turned on, all other choices turned off). Then in the lower left corner, one reads “1111 of 18686 records found”.
Much more sophisticated exploration can be carried out by downloading the rich and flexible Access Database. The terms used for this database were described in detail in three sections of Deep Blue paper associated with this project. The database can be downloaded and explored.
HOW TO USE THE ACCESS DATABASE
1. Click on the Create Cohort and View Math Trait Data button, and select your cohort by clicking on the features of interest (for example: Apron and Blouse).
Note: Depending on how you exited on your previous visit to the database, there may be items to clear up before creating the cohorts.
a) (Usually unnecessary) Click on the small box near the top left corner to allow connection to Access.
b) (Usually unnecessary) If an undesired window blocks part of the screen, click near the top of this window to minimize it.
c) Make certain under Further Filtering that all four Exclude boxes are checked to get rid of stripes and circles, and circular buttons, and the D1 that is trivially associated with shoes.
2. Click on Filter Records to Form the Cohort button. Note the # of designs, # of pieces, and # of costumes beside Recalculate.
3. Click on Calculate Average Math Trait Frequency of Cohort button, and select the symmetry types of interest (for example: D1 and D2) .
4. To view the Stage 1 table, click on Create Stage 1 table. To edit and print this table, click on Create Excel (after table has been created). The same process works for Stages 2, 3.and 4 tables.
5. To view the matrix listing the math category impact numbers, move over to a button on the right side and click on View Matrix of Math Category Impact Numbers. To edit and print this matrix, click on Create Excel, use the Excel table as usual.
This dataset was generated for our work "Shape and symmetry determine two-dimensional melting transitions of hard regular polygons". The dataset includes simulation results for 13 different polygons (equilateral triangles through regular tetradecagons and the 4-fold pentille) at a variety of packing fractions near the isotropic fluid to solid phase transition. Each trajectory contains the final 4 frames of each simulation run we conducted at system sizes of over one million particles.
For each shape, there is a JSON file that describes the vertices of the polygon and a number of simulation trajectory files in GSD ( https://bitbucket.org/glotzer/gsd) format. The trajectory files contain the positions and orientations of all the polygons at each frame, along with the simulation box size. The trajectory file names identify the packing fraction of that simulation run.
This study evaluated the performance of a video-based intervention for improving the belt fit obtained by drivers. Previous laboratory studies have demonstrated that some drivers position their seat belts suboptimally. Specifically, the lap portion of the belt may be higher and farther forward relative to the pelvis than best practice, and the shoulder portion of the belt may be outboard or inboard of mid-shoulder.
A video was developed to present the most important aspects of belt fit best practices, with emphasis on the lap belt. The video demonstrated how a seat belt should be routed with respect to an individual’s anatomy to ensure a proper fit. The three key belt fit concepts conveyed in the video were:
1) Lap belt low on hips, touching the thighs.
2) Shoulder belt crossing middle of collarbone.
3) Belt snug, as close to bones as possible.
Additional context about the ability to achieve to good belt fit, such as opening a heavy coat or adjusting the height adjusters on the B-pillar behind the windows, were also presented.
We provide the parameters used in Umbrella Sampling simulations reported in our study "Efficient Estimation of Binding Free Energies between Peptides and an MHC Class II Molecule Using Coarse-Grained Molecular Dynamics Simulations with a Weighted Histogram Analysis Method", namely the set positions and spring constants for each window in simulations. Two tables are provided. Table 1 lists the names of the peptides and their corresponding sequences. Table 2 lists the parameters. The abstract of our work is the following:
We estimate the binding free energy between peptides and an MHC class II molecule using molecular dynamics (MD) simulations with Weighted Histogram Analysis Method (WHAM). We show that, owing to its more thorough sampling in the available computational time, the binding free energy obtained by pulling the whole peptide using a coarse-grained (CG) force field (MARTINI) is less prone to significant error induced by biased-sampling than using an atomistic force field (AMBER). We further demonstrate that using CG MD to pull 3-4 residue peptide segments while leaving the remain-ing peptide segments in the binding groove and adding up the binding free energies of all peptide segments gives robust binding free energy estimations, which are in good agreement with the experimentally measured binding affinities for the peptide sequences studied. Our approach thus provides a promising and computationally efficient way to rapidly and relia-bly estimate the binding free energy between an arbitrary peptide and an MHC class II molecule.
The ENVIREM dataset v1.0 is a set of 16 climatic and 2 topographic variables that can be used in modeling species' distributions. The strengths of this dataset include their close ties to ecological processes, and their availability at a global scale, at several spatial resolutions, and for several time periods. The underlying temperature and precipitation data that went into their construction comes from the WorldClim dataset ( www.worldclim.org), and the solar radiation data comes from the Consortium for Spatial Information ( www.cgiar-csi.org). The data are compatible with and expand the set of variables from WorldClim v1.4 ( www.worldclim.org).
For more information, please visit the project website: envirem.github.io
Magnetic resonance angiography (MRA) of the aorta of a 30 yo healthy volunteer, segmented and discretized using the software CRIMSON ( www.crimson.software).
Additionally, models corresponding to virtually-aged aortic geometries at ages: 40, 60, and 75.
Transcriptional accessibility of chromatin is central to guiding CD4+ T cell function through regulation of lineage specific gene expression. Myst1 is a histone acetyltransferase responsible for acetylation of the protein tail of histone 4 at lysine residue 16 (H416ac), resulting in increased transcriptional accessibility and activation of gene transcription. Previous studies have described a role for Myst1 in governing lymphocyte development in the thymus, however the role of Myst1 and H4K16ac in guiding activation of peripheral CD4+ T cells has not been studied. Activation of human and murine CD4+ T cells resulted in upregulation of Myst1 expression, and deletion of Myst1 resulted in changes in proliferative responses to both polyclonal stimulus and exogenous cytokines. Myst1-deficient T cells also exhibited modulations in lineage commitment, with decreased function in TH1/TH2 skewing conditions and increased function in response to TH17-promoting conditions. Regulation of Myst1 function in CD4+ T cells appears governed at least in part by STAT5, as Myst1 expression is regulated by STAT5 expression and DNA binding, and modulations in H4K16ac in Myst1-deficient CD4+ T cells is observable at sites in the promoter regions of lineage specific genes following skewing to the TH1 or TH2 lineage in vitro. Taken together, these results indicate an important role for the STAT5-Myst1 epigenetic axis in governing the activation and effector function of CD4+ T cells.